Systems and methods to select targeted advertising

ABSTRACT

Systems and methods to select targeted advertising for display are disclosed. Example systems disclosed herein are to access an address of a media device associated with a household, access purchase data reported from a logging device that logs product purchase activity at the household, determine a consumer segment associated with the household, determine an opportunity metric for a first product to be purchased by the household based on the purchase data reported from the logging device and purchase behavior associated with the consumer segment, select a first advertisement associated with the first product to deliver to the media device based on the opportunity metric and a saturation metric associated with the household for the first advertisement, the saturation metric based on detection of codes embedded in prior instances of the first advertisement delivered to the media device, and transmit the first advertisement to the address of the media device.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. application Ser. No.15/990,341, filed on May 25, 2018, which is a continuation of U.S.application Ser. No. 12/964,481, filed on Dec. 9, 2010, which is anon-provisional application of and claims the benefit of U.S.Provisional Application Ser. No. 61/355,882, which was filed on Jun. 17,2010. Priority to U.S. application Ser. No. 15/990,341, U.S. applicationSer. No. 12/964,481, and U.S. Provisional Application Ser. No.61/355,882 is claimed. U.S. application Ser. No. 15/990,341, U.S.application Ser. No. 12/964,481 and U.S. Provisional Application Ser.No. 61/355,882 are hereby incorporated herein by reference in theirentireties.

FIELD OF THE DISCLOSURE

This disclosure relates generally to targeted advertising and, moreparticularly, to methods and apparatus to select targeted advertising.

BACKGROUND

Product manufacturers and advertisers try to increase demand for theirproducts by influencing the behavior of target consumer segments. Surveyresearch is used to collect information about consumer attitudes andpreferences. Behavioral information, whether observed directly orcollected through survey research, can be used to predict demand. Themanufacturers try to influence consumer preference through use ofadverting strategies to increase demand. A manufacture will try tooptimize its advertising spending by targeting specific consumersegments that represent a high opportunity for the manufacturer toinfluence consumer behavior by raising consumer awareness. Sinceconsumer attitudes and preferences are constantly changing,manufacturers must continually monitor attitudes and preferences topredict demand, as well as continue to influence consumer preferencethrough advertising.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system to provideindividually-targeted advertising to different households based on anopportunity metric.

FIG. 2 is a more detailed block diagram of an example implementation ofthe headend system of the cable provider described in connection withFIG. 1.

FIG. 3A is a block diagram illustrating example data flows that mayoccur in the example systems illustrated in FIGS. 1 and 2.

FIG. 3B illustrates an example advertisement saturation characteristicfor an advertisement of a product with respect to a household.

FIG. 4 is a table illustrating example weekly purchases for severalhouseholds and products.

FIG. 5 is a table illustrating example expected weekly purchases perhousehold by segment for several products.

FIG. 6 is a table illustrating an example advertising log for severalhouseholds and several advertisements.

FIG. 7 is a flowchart representative of example machine readableinstructions which may be executed to select targeted advertising fordisplay.

FIG. 8 is a flowchart representative of example machine readableinstructions which may be executed to determine one or more opportunitymetrics for a household.

FIG. 9 is a flowchart representative of example machine readableinstructions which may be executed to select an advertisement fordelivery to a household.

FIG. 10 is a flowchart representative of example machine readableinstructions which may be executed to determine a media response of ahousehold to a displayed advertisement.

FIG. 11 is a diagram of an example processor system that may be used toexecute the example instructions of FIGS. 7, 8, 9, and 10 to implementthe example systems of FIGS. 1 and 2.

DETAILED DESCRIPTION

Although the example systems described herein include, among othercomponents, software executed on hardware, such description is merelyillustrative and should not be considered as limiting. For example, itis contemplated that any or all of the disclosed hardware and/orsoftware components could be embodied exclusively in dedicated hardware,exclusively in software, exclusively in firmware or in some combinationof hardware, firmware, and/or software.

Cable providers typically sell broadcast advertising time toadvertisers. Targeted advertising is a method of advertising performedby placing advertisements at times and locations where a particular typeof consumer (e.g., a particular demographic segment) that is likely tobe influenced to purchase the advertised product is likely to view theadvertisement. Advertisers often look to increase the effectiveness oftheir advertisements by using targeted advertising.

When considering how much money to spend on an advertisement, anadvertiser will consider the reach of the advertisement (i.e., thenumber of viewers or households who view the advertisement in a givenperiod of time) and the frequency with which the advertisement is shown.As the reach of the advertisement increases, more consumers may view theadvertisement and the number of consumers who may be influenced topurchase a product is increased. However, as the frequency of theadvertisement increases, the advertisement may lose its effectivenessover time. Specifically, repeated showings of an advertisement to aconsumer tend to diminish the effect of the advertisement with eachadditional showing. Thus, additional resources spent on showing a“stale” advertisement may be better spent on other (non-stale)advertisements.

The example systems and methods described herein are useful foreffectively selecting and displaying advertising targeted to individualsand/or households. In some examples, an advertisement is selected fordisplay to a household based on an opportunity metric generated for thehousehold with respect to a product. Different advertisements may beselected for delivery to different households via, for example, a settop box installed at each of the households. The example systems andmethods also consider the likely effectiveness of additional showings ofa particular advertisement to a particular household (e.g., based on anumber of prior showings and historical data showing the relationship(s)between the frequency of showings and effectiveness) and further adjustthe selection of the advertisements to a household based on thepredicted effectiveness. By controlling the selection ofindividually-targeted advertisements as described herein, contentproviders may increase the revenue generated by selling advertisementspace and may improve the effectiveness of advertisement spending byadvertisers.

Example systems and methods described herein select advertisements forindividual household delivery based on a highly individualized anddynamic opportunity metric or score with respect to a product. Previoussystems use proxy methods to measure purchasing such as, for example,measuring television program audiences to approximate the number andtypes of households purchasing a product. However, such approaches donot adapt well in view of purchases of an individual household. Incontrast, the example systems and methods disclosed herein can presentthe most effective advertisements to a particular household, because theselected advertisements change as the household adjusts its purchasinghabits over time. The speed with which the example systems and methodsadjust the opportunity calculations may keep pace with the rate at whichconsumers make their purchases and respond to advertisements.

As used herein, the saturation of a household with respect to anadvertisement refers to the effectiveness of the advertisement forfuture showings of the advertisement. Saturation may be limited toperiods of time, after which the saturation of the household is reset,or may continue indefinitely. When a household is said to have “reached”saturation, future presentations of the advertisement generally havereduced effectiveness relative to earlier presentations.

FIG. 1 is a block diagram of an example system 100 to provideindividually-targeted advertising to different households A 102 and B104 based on an opportunity metric. The example system 100 includes acontent provider 106 such as a cable television company to deliverprogramming and advertisements to a number of households A 102 and B104. The households A 102 and B 104 are provided with respective set topboxes 108 and 110 coupled to televisions 112 and 114. The contentprovider 106 may broadcast programming via a headend system 107 over aplurality of channels, to which the set top boxes 108 and 110 may tuneto receive the broadcast programming. Additionally or alternatively, thecontent provider 106 may provide a library or catalog of programmingand/or advertisements that may be selected for on-demand delivery toeither or both of the set top boxes 108 or 110. The tuned or deliveredprogramming is displayed on the respective televisions 112 and 114. Thetelevision could alternatively or additionally be implemented bypersonal computer monitors or the like.

In the example system 100, the content provider 106 sells time on eachof one or more channels to one or more advertisers. The content provider106 may agree to broadcast particular advertisements to the set topboxes 108 and 110 and, thus, the televisions 112 and 114 for display toconsumers.

Advertisers seek to increase the effectiveness of their advertisingexpenditures by a) increasing the size of the audience that is exposedto their advertisements; and/or b) spending advertising resources in amanner designed to concentrate exposure of the advertisements onsegments of consumers who are more likely to purchase the product beingadvertised. By increasing the audience that views an advertisement, theadvertiser can increase the number of people who are influenced by theadvertisement on the theory that a given advertisement will influence apercentage of people to purchase the product. However, by concentratingan advertisement's exposure on a particular class or segment of peoplewho are more likely to be influenced than the public at large (e.g.,because the product is of more interest to them), each unit ofadvertising resources can influence a relatively larger number of peopleto purchase the product being advertised.

Traditionally, consumers have been grouped or classified by broaddemographic and/or geographic segments. Surveys, shopper membershipcards, and other data collection methods have been used to aidadvertisers in determining when and where to place advertisements.However, these methods relied on massive generalizations ofgeodemographic data to purchasing habits. As a result, these priormethods may only yield nominal statistical improvements in some cases.In contrast, the example system 100 of FIG. 1 includes an opportunitycalculator 118 to determine opportunity metrics for the individualhouseholds A 102 and B 104. The opportunity metrics are representativeof the opportunity a household represents to advertisers of particularproducts based on purchase data 120 and 122 for respective individualhouseholds A 102 and B 104 and expected purchase data 124.

The household purchase data 120 and 122 identifies purchases made by therespective example households A 102 and B 104. The purchase data 120 and122 may be provided to the opportunity calculator 118 by, for example,self-reporting purchases, using a purchase logging device to identifyand/or log purchases' (e.g., a Nielsen HomeScan® device used in paneltracking), and/or any other reporting method and/or combination ofreporting methods. Self-reporting may occur via filling out a surveyand/or keeping a manual and/or electronic log of purchases.

Another example method to collect purchase data by household may includecollecting frequent shopper card data. Many retail stores currentlyissue frequent shopper cards to persons who volunteer personalinformation. The personal information may include any one or more of theperson's address, the person's personal or household demographics, theperson's personal preferences, and/or any other voluntarily-providedpersonal data. A frequent shopper card is scanned whenever the carrierof the frequent shopper card makes purchases at the issuing retailer,and the purchased items are logged and attributed to the carrier of thefrequent shopper card. In exchange for the personal information andmonitoring ability provided by the frequent shopper card, the retaileroffers discounts on purchases to the carrier of the card. The retailermay then use the collected purchase data to identify patterns or performother data operations to obtain useful shopper data.

The expected purchases, as described in more detail below, may bedetermined by a collaborative filter 126 that determines a quantity of aproduct that the households A 102 and B 104 might be expected to consume(e.g., per day, per week, per month, per year) based on the segment(s)in which the households A 102 and B 104 may be classified and what thosesegment(s) typically purchase. By determining the difference betweenwhat quantity of a given product the respective households A 102 and B104 are expected to purchase (e.g., expected purchases 124 of FIG. 1)and what quantity of that product the households A 102 and B 104actually purchased as reflected in the purchase data 120 and 122, theopportunity calculator 118 generates an opportunity metric. Theopportunity metric is provided to the content provider 106 to enable thecontent provider 106 to select the most appropriate (e.g., effective)advertisements to be delivered to the households A 102 and B 104.

To evaluate the relationships between different items, the collaborativefilter 126 may be populated and/or updated with the purchasingrelationships between different goods and/or brands. The collaborativefilter 126 may be maintained by, for example, a research service oragency that performs market research of consumer segments and behaviors.

FIG. 2 is a more detailed block diagram of an example headend system 200to implement the headend system 107 of the content provider 106described in connection with FIG. 1. The headend system 200 may be usedto select and display advertisements for individual households (e.g.,the households A 102 and B 104) based on one or more opportunitymetrics. For clarity and brevity, the following description of FIG. 2will reference an example where the headend system 200 considers thehousehold A 102 of FIG. 1. The example headend system 200 includes anadvertisement selector 202 to select an advertisement for delivery to ahousehold via a media deliverer 204. The media deliverer 204 may beimplemented via, for example, a cable broadcast system. In theillustrated example, the media deliverer 204 is in communication withmultiple subscriber set top boxes (e.g., the set top boxes 108 and 110of FIG. 1).

The advertisement selector 202 is provided with an advertisementdatabase 206, from which the advertisement selector 202 may chooseadvertisements associated with one or more products. The advertisementsmay be provided to the database 206 by one or more advertisers (e.g.,the advertisers 116 of FIG. 1) who wish to have their productadvertisement(s) targeted at households that have a high opportunitymetric corresponding to the product(s) being advertised.

The advertisement selector 202 receives several inputs to determine anadvertisement that should be delivered to a particular household. Forexample, the advertisement selector 202 receives the selection of ahousehold A 102 from a household selector 208. The selection of thehousehold focuses the advertisement selector 202 on the household A 102that should be considered for individual advertisement delivery. Asmentioned above, the advertisement selector 202 receives the opportunitymetric(s) for one or more products. Because the opportunity metric(s)are specific to an individual household, the advertisement selector 202considers the opportunity metric(s) corresponding to the householdselected by the household selector 208.

In the illustrated example, the advertisement selector 202 also receivesprogramming association information from an ad/programming associator210. The ad/programming associator 210 identifies advertisements forproducts in the advertisement database 206 which are appropriate forprogramming currently shown to the household A 102. For example, if afirst advertisement and a second advertisement in the advertisementdatabase correspond to equal or similar opportunity metric(s) for thehousehold A 102, the associator 210 may identify an association betweenthe first advertisement and the program being viewed at the householdthat causes the selector 202 to select the first advertisement fordelivery. For example, if a young children's program is being viewed atthe household A 102, the associator 210 may indicate that a firstadvertisement for cereal has a more appropriate association with theprogram than a second advertisement for coffee. In such a case, theselector 202 may give weight to the association and select theadvertisement for cereal.

The example advertisement selector 202 further receives advertisementresponse data for the household A 102 from a media response evaluator212. The media response evaluator 212 monitors the advertisements thatare shown to the household A 102 (e.g., that are sent from theadvertisement selector 202 to the media deliverer 204 and/or that aresent to the household A 102 during the course of regular programming andadvertisement) and reports to the selector 202 whether the advertisementbeing considered has been shown to the household A 102 enough to causethe advertisement to lose its effectiveness with respect to thehousehold A 102. For example, when the advertisement selector 202 sendsa selected advertisement to the media deliverer 204 for delivery to thehousehold A 102, the selected advertisement (or, alternatively, metadataidentifying the advertisement) is also sent to the media responseevaluator 212. The media response evaluator 212 uses one or moremarketing mix models or saturation algorithms to determine whether thehousehold A 102 has been presented with the advertisement often enoughto reduce the effectiveness of the advertisement on futurepresentations.

As an example of the operation of the media response evaluator 212,assume that the advertisement selector 202 is considering twoadvertisements: one for product X and one for product Y for delivery tothe household A 102. The opportunity metric for the household A 102associated with product X is higher than the opportunity metricassociated with the product Y. However, the advertisement associatedwith product X has been shown to the household A 102 many times, whilethe advertisement associated with product Y has not yet been shown tothe household A 102. As a result, the media response evaluator 212determines that the effectiveness of the advertisement for product X isreduced because the household A 102 has become saturated with themessage presented by the advertisement. In contrast, the effectivenessof an advertisement for product Y is still relatively high (given thelower opportunity metric for product Y). The calculated effectiveness ofshowing each advertisement may then be correlated to a price to chargean advertiser to show its advertisement.

In operation, the example headend system 200 receives opportunitymetric(s) that are individualized for several households. The householdselector 208 selects one of the households to be considered by theadvertisement selector 202. The advertisement selector 202 receives(e.g., requests, retrieves from storage, etc.) the opportunity metric(s)associated with the selected household. The advertisement selector 202evaluates the products for which an opportunity metric is provided anddetermines whether the household is saturated with the advertisement(s)for any of the product(s). Saturation information may be requested fromthe media response evaluator 212, which determines the saturation and/orthe future media response of the household to the potentialadvertisement(s). The media response evaluator 212 may perform theevaluation on request from the advertisement selector 202 and/or havepreviously prepared evaluations stored. For those advertisement(s) thathave reached saturation, the advertisement selector 202 applies apenalty.

The advertisement selector 202 loads a pricing structure, pricingcharacteristic, or pricing framework from the pricing database 214 anddetermines, based on the opportunity metric(s) and the saturation of theadvertisements, which advertisement(s) may command the highest pricefrom advertisers due to the likelihood that the advertisement(s) will beeffective. The advertisement selector 202 provides the selectedadvertisement(s) to the media deliverer 204, which determines the settop box address associated with the selected household from the addressdatabase 216 and delivers the advertisement(s) to the address at theappropriate time. The advertisement(s) are further provided to the mediaresponse evaluator 212 to determine the response of the selectedhousehold to further presentations of the selected advertisement(s).

FIG. 3A is a block diagram illustrating example data flows that mayoccur in the example systems illustrated in FIGS. 1 and 2. The exampledata flows illustrated in FIG. 3A show the exchanges of data between thehousehold A 102, the content provider 106, the set top boxes 108 and110, and the advertisers 116. While the example household A 102 isillustrated in FIG. 3A, the example exchanges of data may be used in asimilar or identical manner with respect to additional households (e.g.,the household B 104). The data flows 300 of FIG. 3A further detail theexchange of data between the advertisement selector 202, the mediadeliverer 204, the advertising database 206, the household selector 208,the ad/programming associator 210, the media response evaluator 212, thepricing database 214, and the address database 216 within the exampleheadend system 200 of FIG. 2.

For clarity and brevity, the example data flows 300 of FIG. 3A willrefer to the selection of advertisement(s) for delivery to the householdA 102 of FIG. 1 without regard to the household B 104. However, theexamples described herein may be extended to include any number ofhouseholds.

The example household A 102 of FIG. 1 is associated with purchase data302 (e.g., the products and quantities purchased by the household duringa given time period, etc.) and geodemographic data 304 (e.g., thehousehold geographic location, the number of persons in the household,the number of children, the ages of the persons in the household, etc.).The purchase data 302 and the geodemographic data 304 may be providedby, for example, self-reporting by the household A 102 and/or monitoringtechniques practiced within the household A 102. The purchase data 302may additionally or alternatively be provided through the use offrequent shopper card data associated with frequent shopper cardscarried by members of the household A 102.

Based on the purchase 302 and demographic data 304, the household A 102fits into a segment of consumers. Members of the consumer segment areidentified as part of other marketing studies and their purchases aretracked over time. Consumer segment data 306 may be developed and/orupdated to reflect, for example, new product offerings, purchasingtrends, and/or changes in membership characteristics. Additionally,retailer purchase panel data 308 is collected at different points ofsale. In some examples, the product purchase data 308 is collected fromas many retailers or other points of sale as possible. Additionally oralternatively, the retailer purchase panel data 308 is received from adata processing facility that has collected, aggregated, and/orprocessed purchase data from multiple retailers (e.g., a set ofretailers in one or more geographic areas). The processed purchase datamay be filtered to provide data that is particularly useful todetermining the expected purchases of the household A 102 and/or thesegment.

The purchase data 302 and the geodemographic data 304 from the householdA 102, the relevant consumer segment data 306, and the relevant retailerpurchase panel data 308 are input to a collaborative filter 126. Thepurchase data 302 provided to the collaborative filter 126 may includeproduct identifiers and quantities per time period. The geodemographicdata 304 of the illustrated example includes sufficient geodemographicinformation to place the household A 102 into a consumer segment. Theexample consumer segment data 306 of the illustrated example provides atleast the segment purchase information of the products purchased by thehousehold A 102. The consumer segment data 306 may additionally provideavailable product purchase information for the consumer segment intowhich the household A 102 fits. The retailer purchase panel data 308provides bills of sale that illustrate the combinations of products thatare often purchased together.

The collaborative filter 126 receives the purchase data 302 and thegeodemographic data 304 from the household A 102, the consumer segmentdata 306, and the retailer purchase panel data 308, and determines oneor more opportunity metric(s) for one or more products based on thereceived information. For example, the purchase data 302 includes aquantity of a product X (e.g., a brand of a product) purchased weekly bythe household A 102. The geodemographic data 304 allows thecollaborative filter 126 (or some other classification device) togenerate a purchase expectation for the household A 102 based onplacement of the household A 102 into a segment (e.g., segment N). Theplacement of the household A 102 into the segment N may be based onother data besides geodemographic data, such as the purchase data 302.

The consumer segment data 306 provides the typical (e.g., average)weekly purchases of one or more products, including product X, byhouseholds in the segment N. Thus, the collaborative filter 126 maydetermine the amount by which the household A 102 is below or above thetypical weekly purchases for the segment N. Retailer purchase panel data308 allows the collaborative filter 126 to evaluate the combinations andquantities of products that are often purchased together, which may thenbe applied to the purchase data 302 to determine any other products thatthe household may be interested in based on its purchases of product Xand the historical purchasing trends associated with the segment N.

Unlike known collaborative filters, the example collaborative filter 126does not assume that once a product is purchased, the product no longerneeds to be purchased again (or that the product does not need to bepurchased long enough to assume no further purchase is desired). Such anassumption may be appropriate when recommending media, toys, consumerelectronics, books, and/or other durable goods and/or items for whichone purchase is often sufficient, based on previous purchases of suchitems. Instead, the collaborative filter 126 of FIG. 3A determinesrecommended or high-opportunity items based on volumetric purchases suchas foodstuffs, cleaning supplies, personal hygiene items, and/or otherconsumable items which are purchased and consumed with some regularity.However, the collaborative filter 126 may also determine or account forproducts that are generally purchased infrequently.

The collaborative filter 126 determines, based on the purchases 302 ofthe household A 102 and historical purchasing trends of the relatedsegment N, items that may be desirable substitutes for and/orsupplements to products the household A 102 currently purchases. Bydetermining the segment (e.g., N) of the household A 102, thecollaborative filter 126 determines the quantity of the product X thatis typically purchased by households in the segment N, the additionalquantity of the product X that the household A 102 should be buyingbased on its segment if the quantity purchased by the household A isbelow the typically purchased quantity, and similar products andquantities purchased by households in the segment N which have not beenpurchased or have been under-purchased by the household A 102. Thecollaborative filter 126 further determines additional products that maybe similar or dissimilar to the product X that are often purchased byhouseholds in the segment N based on the retailer purchase panel data308. In some examples, the retailer purchase panel data 308 is at leastpartially used to determine the consumer segment data 306.

The collaborative filter 126 outputs one or more opportunity metrics 312for each product (e.g., products X, B, C, and D) that are identified ashaving an opportunity associated with the household A 102. Theopportunity metric of a product X is based on the purchase data 302 ofthe household A, the segment N in which the household A is located, andthe typical purchases of the product X, the product's substitutes,and/or the product's complements by households in the selected segmentN.

For example, assume the household A 102 purchases thirty-six cans ofsoft drink D per week, households in the segment N purchase an averageof twenty-four cans of soft drink X per week and twenty-four cans ofsoft drink D per week. Additionally, assume the household A 102purchases zero ounces of potato chips B per week and members of segmentN average purchases of sixteen ounces of potato chips B per week. Inthis example case, the collaborative filter 126 determines that theopportunity for motivating the household A 102 to increase its purchasesof soft drink D is low because it currently purchases more of soft drinkD than is typical for segment N. In contrast, the opportunity for softdrink X is high, because the household A 102 purchases none of softdrink X compared to the average of 12 cans of soft drink X per week.Therefore, advertising soft drink X to the household A 102 may be highlylikely to influence the household A 102 to increase its purchases ofsoft drink X. Additionally, the collaborative filter 126 determines thatthe opportunity for potato chips B is high because the average purchasesfor potato chips B among households in segment N is sixteen ounces perweek and household A is currently not purchasing chips. Thus, anadvertisement for potato chips B may have a relatively higher likelihoodto influence the household A 102 to increase its purchases of potatochips B. If desired, the purchase data may be supplemented withuser-specific data collected via, for example, a survey reflecting userpreferences, dietary habits, medical conditions, allergies, etc. Thissupplemental data may be factored in by the collaborative filter 126.

The opportunity metric(s) 312 developed by the opportunity calculator118 of FIG. 1 are input to the advertisement selector 202 of FIG. 2. Inaddition to the opportunity metric(s) 312, the advertisement selector202 receives pricing information 314 (e.g., from the pricing database214 of FIG. 2), advertisement/programming associations 316 (e.g., fromthe ad/programming associator 210 of FIG. 2), advertisements 318 (e.g.,from the advertisement database 206 of FIG. 2), and household saturationinformation (e.g., a saturation metric) from an advertising responsemonitor 320 (e.g., from the media response evaluator 212 of FIG. 2). Theselection of the household A 102 (e.g., from the household selector 208of FIG. 2) is identified expressly or implicitly in the delivery of theopportunity metric(s) 312 for household A 102.

While the example opportunity metric(s) 312 are shown in FIG. 3A as ascore, the opportunity metric(s) 312 may be presented or measured in anysuitable manner. For example, the opportunity metric(s) of the householdA 102 with respect to a product may be represented in terms of anormalized or gross score, a monetary amount (e.g., dollars/year), unitsof product (e.g., ounces/year), or any other appropriate unit or score.

The pricing information 314 may include, for example, a function basedon the opportunity metric(s) of a particular product, a pricingstructure based on a client and/or volume of advertisements, or otherpricing structure, characteristic, and/or factors. In some examples, thepricing information 314 includes a function that increases the price ofdelivering a particular advertisement to a particular household or to anumber of households based on the opportunity metric(s) associated withthe advertised product and the household.

In some example advertisement pricing models, a media provider (e.g.,the media provider 200 of FIG. 2) contracts with an advertiser (e.g.,the advertisers 116) to provide an advertisement with a certain numberof viewers. The number of viewers may be calculated using, for example,reach and frequency numbers. Certain measures of advertisement exposure(e.g., gross ratings points) may consider showing an advertisement onceto each person in a population to be equivalent to showing theadvertisement twice each to half of the people in the population. Insome examples, the media provider 200 may agree to provide anadvertisement with a specified reach and frequency to households havinga minimum opportunity metric for a price premium reflecting the improvedadvertising opportunity to the advertiser 116. The advertiser 116 maydetermine that the premium is acceptable, or even a bargain, to targetfewer households having a high opportunity metric instead of targeting amore general audience. However, many different methods and models ofadvertisement pricing based on the opportunity metric are available andare considered within the scope of the examples described herein.Pricing methods and models may be easily modified to improve revenue toboth the media provider 200 and the advertisers 116.

In some examples, the media provider 200 may determine the opportunitymetric for a type of product as opposed to a particular brand. Forexample, cola is a type of product where Coke® and Pepsi® are particularbrands. To increase revenue, the media provider 200 may solicit bidsfrom the manufacturers of different colas on reach and frequencyagreements for advertising priority to households having the highestopportunity metrics for cola.

The advertising/programming associations 316 may include, for example,broadcast programs that advertisers identify as preferable to identifywith advertised products. For example, the ad/programming associations316 may include an association between a breakfast cereal C and achildren's program specified by the advertiser or manufacturer ofbreakfast cereal C. These associations may be stored in a table and maybe manually input based on data and/or requests from advertisers,broadcasters, and/or content creators.

The advertisements 318 include at least the advertisements for productshaving an opportunity metric provided by the collaborative filter 126.In some examples, the advertisements 318 include multiple differentadvertisements (e.g., variants of an advertisement or completelydifferent advertisements) that may be shown to the household for thesame product. Thus, the advertisement selector 202 may show differentadvertisements for the same product to the household A 102, therebydecreasing the saturation of the household A 102 to advertisements for aproduct. By changing the advertisements for a product, the advertisementselector 202 may maintain higher revenue for an advertisement mix sentto the household A 102 by maintaining a high effectiveness of theadvertising mix.

The ad response monitor 320 monitors the advertising sent to thehousehold A 102. The ad response monitor 320 may monitor only householdA and/or may be part of a larger section which monitors broadcastadvertising sent to program viewers in a geographic area of interest.Broadcast advertisements may be identified in any manner, such as byreading an identification code embedded in (or otherwise broadcast with)the broadcast advertisement. Based on the advertising sent to thehousehold A 102, the ad response monitor 320 determines saturationmetric(s) of the household A 102 with respect to one or moreadvertisements. As the number of times a particular advertisement ispresented to household A 102 increases, the ad response monitor 320determines that the incremental effectiveness of that advertisementdecreases with additional showings (i.e., the total effectivenessincreases more slowly). The example ad response monitor 320 thusprovides a penalty to be applied to certain advertisements 318 that maybe selected by the advertisement selector 202 when the advertisements318 have been shown a sufficient number of times. Additionally,consumers with different demographics may tend to have different adresponse characteristics or the ad response characteristics used by thead response monitor 320 may change over time. Therefore, the ad responsemonitor 320 may be provided and/or updated by, for example, a consumerresearch service or agency that specializes in consumer behavior.

As the number of times an advertisement 318 is shown increases beyond asaturation point, the ad response monitor 320 increases the penalty. Insome examples, however, the penalty may decrease from the firstpresentation to the second and/or third (and/or additional)presentations and then increase for presentations after the third (orlater) presentation. One or more saturation characteristic(s) 324provide models for the ad response monitor 320 to apply the penalty. Anexample of such a saturation characteristic is illustrated in FIG. 3B.Thus, while advertisements for products having mid- to high-rangeopportunity metrics with respect to the household A 102 may besufficiently high to overcome the penalties and, thus, continues to beshown, after an advertisement 318 has been shown often enough the pricefor that advertisement 318 will be overtaken by another advertisementoffering a higher price because it is expected to exhibit higheradvertising effectiveness.

When the advertisement selector 202 has received the pricing information314, the advertisement/programming associations 316, the advertisements318, and the saturation metric(s) from the ad response monitor 320, theadvertisement selector 202 selects an advertisement for delivery and/orpresentation to the household A 102 (e.g., via the set top box 108 andthe television 112 of FIG. 1). The selected advertisement and thedestination for the advertisement (e.g., household A 102) are providedto the media deliverer 204. The media deliverer 204 also receiveshousehold address information 322 corresponding to the set top box 108in the household A 102. The address information may include, forexample, a media access control (MAC) address, an Internet protocol (IP)address, or any other type of network layer or other type of addressthat uniquely identifies the set top box 108 of household A 102. Themedia deliverer 204 delivers the selected advertisement to the set topbox 108 in household A 102 at the appropriate time, such as shortlyprior to the time space sold by the content provider 200.

The media deliverer 204 may also be responsible for broadcasting (e.g.,to a large portion of the possible audience) programming and/oradvertisements to the households 102 and 104. In addition to providingthe selected advertisements (e.g., advertisements individually selectedfor a household) and broadcast advertisements (e.g., advertisements notindividually selected for a household) to the household A 102, the mediadeliverer 204 further provides and/or identifies the selectedadvertisements and broadcast advertisements to the ad response monitor320. As mentioned above, the ad response monitor 320 monitors theadvertisements presented to household A 102 and provides householdsaturation information (e.g., a saturation metric) to the advertisementselector 202 to select future advertisements for delivery to thehousehold A 102. Thus, the ad response monitor 320 uses the identifiedadvertisements to update the saturation metric or level.

The advertisements provided to the set top box 108 and/or presented tothe household A 102 may be further fed back to the advertisementselector through advertisement-driven purchasing by the household A 102.For example, when an advertisement stimulates purchases of product Xthat previously had a high opportunity metric, the collaborative filter126 may determine that the opportunity metric for the household A 102for product X decreases because the quantity of product X that thehousehold A 102 is expected to purchase has been constant for therelevant time period, unless the household A 102 changes segment or thebehavior data associated with the segment changes as may happen overtime (e.g., seasonally). As a result, the advertisements for product Xbecome less effective and new products may be advertised to thehousehold A 102 at a higher effectiveness and, thus, a higher price.

The example data flows illustrated in FIG. 3A may be performed by anyone or more parties. In some examples, a cable or other media provider(e.g., the content provider 106) of FIG. 1 may implement at least theadvertisement selector 202, the media deliverer 204, and the ad responsemonitor 320 to deliver media and advertisements to households and tomaintain a high degree of responsiveness to saturation of households. Insome examples, a media research organization may collect and/or processthe purchase data 302, the geodemographic data 304, the consumer segmentdata 306, the retail purchase data 308, and/or implement thecollaborative filter 126 to generate opportunity metrics. The mediaresearch organization then provides the opportunity metrics to a contentprovider to select and deliver advertisements. In some examples, themedia research organization may additionally provide and/or update thesaturation characteristic(s) 324 to improve the selection ofadvertisements. While the data flows may be implemented by any one ormore parties, the examples described above may leverage existingexpertise and relationships to improve service to advertisers andconsumers.

FIG. 3B illustrates an example advertisement saturation characteristic324 for an advertisement of a product X with respect to the examplehousehold A 102. The horizontal axis is representative of the number ofpresentations of the advertisement to the household A 102. The verticalaxis is representative of the expected likelihood that the household A102 will exhibit a response or behavior (e.g., purchasing the advertisedproduct). As illustrated in FIG. 3B, the expected likelihood of aresponse increases more rapidly between the second presentations and thethird presentation than for other presentations (e.g., between the firstand second presentations, between the fourth and fifth presentations,etc.).

The example saturation characteristic 324 may be provided to, forexample, the ad response monitor 320 to determine a saturation metricfor the product X and the household A 102. The ad response monitor 320determines the number of presentations of the advertisement to thehousehold A 102 and generates a saturation metric (e.g., a penalty). Thead response monitor 320 provides the saturation metric to theadvertisement selector 202, which may apply (e.g., subtract, multiply,etc.) the saturation metric to the corresponding opportunity metric togenerate a net effectiveness metric. Different advertisements for thesame product X may have different net effectiveness metrics depending onthe number of times the respective advertisements have been presented tothe example household A 102. The advertisement selector 202 may thenselect an advertisement for delivery to the household A 102 by, forexample, comparing the net effectiveness metrics to a threshold. In someexamples, the threshold is determined by an agreement with anadvertiser. However, the net effectiveness metrics may be used toidentify an advertisement for delivery in any appropriate manner.

According to the example saturation characteristic 324, the ad responsemonitor 320 may cause the saturation metric of the household A 102 tothe advertisement to be higher after the household A 102 has beenpresented the advertisement five times than after the household A 102has been presented the advertisement two times. However, according tothe saturation characteristic 324, the saturation metric after thehousehold A 102 has been presented the advertisement once may be verysimilar to the saturation metric after the household A 102 has beenpresented the advertisement five times.

The example saturation characteristic 324 may be represented by theequation y=1+e^(A+Bf), where y is the expected likelihood of response, fis the frequency with which presentations of an advertisement arepresented to a household, and A and B are variables that may bedetermined empirically by, for example, a media research organization.

While an example saturation characteristic 324 is illustrated in FIG.3B, saturation characteristics 324 may be additionally and/oralternatively represented by, for example, a mathematical algorithm, alookup table, or any other appropriate representation. Further, thesaturation characteristic 324 may differ between household, segment,and/or product combinations.

FIG. 4 is a table 400 illustrating example expected weekly purchases perhousehold by segment. The example table 400 may be representative of theconsumer segment data 306 of FIG. 3A for example segments N, Q, R, S,and T. The table 400 includes the expected purchases of severalproducts, including beer Z, chips B, bread H, bread J, and milk M. Theexpected purchases provided by table 400 may be used by thecollaborative filter 126 to determine opportunity metrics with respectto the products Z, B, H, J, and/or M for households belonging to thesegments N, Q, R, S, and/or T. The example product references Z, B, H,J, and M are representative and would normally be replaced with brandnames of corresponding products and/or other identifiers such as the UPCor the SKU of a product.

FIG. 5 is a table 500 illustrating example weekly purchases for severalhouseholds by product. The example table 500 may be provided to thecollaborative filter 126 and used in combination with the example table400 to determine the opportunity metrics of the households 1-5 listed inthe table 400 with respect to the listed products. The example table 500further includes the segment that each household 1-5 falls into based onsimilar purchases and/or geodemographic information.

To generate the opportunity metrics for the households 1-5, thecollaborative filter 126 compares the purchases of each of the relevantproducts in the example weekly (or other time period) purchases table500 with the expected purchases of the corresponding products found inthe appropriate segment row of the expected purchases table 400. Forexample, the collaborative filter 126 compares the weekly purchases ofchips B for household 1, which is in segment N, with the expected weeklypurchases of chips B by households in segment N in the expectedpurchases table 400. If the weekly purchases and the correspondingexpected purchases are similar or identical, the example opportunitymetric corresponding to the product will be low. In contrast, if theweekly purchases of the product are lower than the expected purchases,the opportunity metric for that product with respect to that householdwill be high.

FIG. 6 is a table 600 illustrating an example advertising log forseveral households and several advertisements. The example table 600 maybe maintained by the example media response evaluator 212 of FIG. 2 toevaluate the media response of different households 1-5 at theappropriate times. In the example table 600, the advertisements 1 and 2represent broadcast advertisements that are shown to each of thehouseholds 1-5 during regular programming and advertising times, whichmay not be directly under the control of the content provider 106. Incontrast, the example advertisement 3 is targeted at the households 1and 4 using the example system(s) and/or method(s) disclosed herein inaddition to being shown as part of regular broadcast programming to allhouseholds 1-5. The remaining advertisements 4-11 are targetedadvertisements presented to the individual households 1-5 duringadvertising time owned by the content provider 106 and not presented aspart of a blanket advertising campaign. Any number of advertisementsand/or households may be stored in the example table 600 by the mediaresponse evaluator 212 and used to evaluate media response toadvertisements presented to the households.

When evaluating the media responses according to the example table 600,the media response evaluator 212 considers saturation to begin afterthree presentations of a given advertisement. However, any statisticallyor otherwise determined number of presentations may be used. Thus, inthe example of FIG. 6, when the media response evaluator 212 evaluatesadvertisements to be delivered to household 4, the media responseevaluator 212 applies a penalty to advertisements 1, 2, and 9.

While an example manner of implementing the system 100 and the headendsystem 107 has been illustrated in FIGS. 1, 2 and/or 3A, one or more ofthe elements, processes and/or devices illustrated in FIGS. 1, 2 and/or3A may be combined, divided, re-arranged, omitted, eliminated and/orimplemented in any other way. Further, the example set top boxes 108and/or 110, the example content provider 106, the example headend system107, the example opportunity calculator 118, the example collaborativefilter 126, the example advertisement selector 202, the example mediadeliverer 204, the example advertisement database 206, the examplehousehold selector 208, the example advertisement/programming selector210, the example media response evaluator 212, the example pricingdatabase 214, the example address database 216 and/or, more generally,the example system 100 may be implemented by hardware, software,firmware and/or any combination of hardware, software and/or firmware.Thus, for example, any of the example set top boxes 108 and/or 110, theexample content provider 106, the example headend system 107, theexample opportunity calculator 118, the example collaborative filter126, the example advertisement selector 202, the example media deliverer204, the example advertisement database 206, the example householdselector 208, the example advertisement/programming selector 210, theexample media response evaluator 212, the example pricing database 214,the example address database 216 and/or, more generally, the examplesystem 100 could be implemented by one or more circuit(s), programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)), etc. When any of the appended claims are read tocover a purely software and/or firmware implementation, at least one ofthe example set top boxes 108 and/or 110, the example content provider106, the example headend system 107, the example opportunity calculator118, the example collaborative filter 126, the example advertisementselector 202, the example media deliverer 204, the example advertisementdatabase 206, the example household selector 208, the exampleadvertisement/programming selector 210, the example media responseevaluator 212, the example pricing database 214, and/or the exampleaddress database 216 are hereby expressly defined to include a tangiblemedium such as a memory, DVD, CD, etc. storing the software and/orfirmware. Further still, the example system 100 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1, 2, and/or 3, and/or may include more than one ofany or all of the illustrated elements, processes and devices.

FIG. 7 is a flowchart representative of example machine readableinstructions 700 which may be executed to implement the example system100 of FIGS. 1-3A. The example instructions 700 may be executed by, forexample, the processor 1102 of FIG. 11 to implement the headend system200 illustrated in FIG. 2 to deliver targeting advertising to individualhouseholds. In some examples, the instructions 700 may be executedseparately for each household for which targeted advertising will beselected and delivered. The example instructions 700 begin bydetermining one or more opportunity metric(s) for one or more productsfor a household (e.g., the household A 102 or the household B 104 ofFIG. 1) (block 702). An example process to implement block 702 isdescribed in more detail below with reference to FIG. 8.

The headend system 200 (e.g., via the advertisement selector 202 of FIG.2) selects an advertisement for display or presentation to the householdbased at least on the opportunity metric of the product associated withthe advertisement (block 704). The selection of the advertisement may bebased further on a pricing structure, an advertisement/programmingassociation, and/or a saturation characteristic of the household withrespect to different advertisements. An example process to implementblock 704 is described in more detail below with reference to FIG. 9.The headend system 200 (e.g., via the media deliverer 204 of FIG. 2)delivers the selected advertisement to the household (block 706). Themedia deliverer 204 may deliver the selected advertisement during a timeslot owned by the content provider 106. In some examples, the mediadeliverer 204 receives an address corresponding to a set top box (e.g.,the set top box 108 in the household A 102 of FIG. 1) and transmits theadvertisement to the address.

The headend system 200 (e.g., via the media response evaluator 212 ofFIG. 2) then determines the media response of the household to theadvertisement (block 708). For example, the media response evaluator 212may determine that future presentations of the delivered advertisementare likely to exhibit decreased effectiveness because the household hasbeen saturated with the advertisement. An example process to implementblock 708 is described in more detail below with reference to FIG. 9.The media response evaluator 212 then feeds back the media response tothe advertisement selector 202 (block 710). The advertisement selector202 may use the media response in selecting future advertisements fordelivery to the household (e.g., future iterations of block 704).

FIG. 8 is a flowchart representative of example machine readableinstructions 800 which may be executed to determine one or moreopportunity metrics for a household. The example instructions 800 may beexecuted by, for example, the processor 1102 of FIG. 11 to implement thecollaborative filter 126 of FIG. 3A and/or block 702 of FIG. 7.

The example collaborative filter 126 receives purchasing information(e.g., the purchasing information 302 of FIG. 3A) and geodemographicinformation (e.g., the geodemographic information 304 of FIG. 3A) from ahousehold (e.g., the household A 102 of FIG. 3A) (block 802). Thecollaborative filter 126 further receives purchasing and geodemographicdata from one or more retail panels (block 804). In some examples, block804 may be accomplished by receiving aggregate and/or processed frequentshopper card purchase data representative of multiple retailers in ageographic area of interest. By receiving the aggregate data, theexample collaborative filter 126 may receive data specific to thesegment and/or geographic area to which the household A 102 belongs. Thecollaborative filter 126 determines the segment to which the household A102 belongs based on the purchase information 302 and the geodemographicinformation 304 (block 806). In some examples, the segments aredetermined based on the purchase information 302, the geodemographicinformation 304, and/or the data received from retail panels.Alternatively, the segments may be predetermined by an outside provider.The collaborative filter 126 may use additional and/or alternative datato determine the household segment.

The collaborative filter 126 then identifies products for which anopportunity metric may be calculated (block 808). Products identified bythe collaborative filter 126 may include products purchased by thehousehold A 102 and/or complements to such products. For example, thecollaborative filter 126 may determine, based on the purchase data fromdifferent points of sale, that particular products purchased by thehousehold A 102 have popular substitutes and/or complementary products.For instance, the collaborative filter 126 may determine that, based onthe points of sale data, purchasers who purchase a particular brand ofpotato chips also tend to purchase a particular brand or flavor of chipdip as a complementary item. Similarly, the collaborative filter 126 maydetermine that purchasers who buy combinations of certain cereals andcandy also tend to purchase a particular brand of flavored drink mix.

The collaborative filter 126 selects one of the products identified inblock 808 to determine an opportunity metric for the product withrespect to the household A 102 (block 810). Based on the points of saledata and the segment of the household A 102, the collaborative filter126 determines the expected and/or average purchases of the selectedproduct for the household A 102 and/or the segment to which thehousehold A 102 belongs (block 812). Based on the expected and/oraverage purchases of the selected product and the current purchases ofthe selected product by the household A 102, the collaborative filter126 determines the opportunity metric for the selected product in theselected household A 102 (block 814). The opportunity metric is based onthe difference between the expected purchases (e.g., weekly) by thehousehold A 102 and the actual purchases by the household A 102.

The collaborative filter 126 then determines whether there areadditional identified products for which an opportunity metric must begenerated (block 816). If there are additional products (block 816),control returns to block 810 to select another identified product.Blocks 810-816 iterate to determine opportunity metric(s) for theproducts identified in block 808. When there are no more products forwhich an opportunity metric is to be generated (block 816), controlreturns to block 704 of FIG. 7.

FIG. 9 is a flowchart representative of example machine readableinstructions 900 which may be executed to select an advertisement fordelivery to a household. The example instructions 900 may be executedby, for example, the processor 1102 of FIG. 11 to implementadvertisement selector 202 of FIG. 2 and/or block 704 of FIG. 7.

The advertisement selector 202 first selects a household and receivesopportunity metric information for the household A 102 (e.g., thehousehold A 102 of FIG. 1) (block 902). The household A 102 may beselected based on opportunity metric information received from, forexample, the collaborative filter 126. The advertisement selector 202selects a first product from the opportunity metrics associated with thehousehold A 102 (block 904). For example, the advertisement selector 202may select the product having the highest opportunity metric withrespect to the household A 102 or may select a product at random from alist of products for which an opportunity metric is provided.

The advertisement selector 202 then determines whether the opportunitymetric associated with the selected product is greater than a threshold(block 906). If the opportunity is not greater than the threshold (block906), control returns to block 904 to select another product. If theopportunity metric for the selected product is greater than thethreshold (block 906), the advertisement selector 202 selects anadvertisement associated with the selected product (block 908). Forexample, the advertisement selector 202 may select an advertisementassociated with the product from the advertisement database 206 of FIG.2. In some examples, the advertisement database 206 includes multipleadvertisements associated with a particular product and theadvertisement selector 202 picks between the same as explained below.

The advertisement selector 202 determines the saturation of thehousehold A 102 with respect to the selected advertisement (block 910).For example, the advertisement selector 202 may have previously receiveda count or other indication of a number of times that the selectedadvertisement has been presented to the household A 102. Presentation ofthe advertisement to the household A 102 may be counted as a result ofgeneral broadcast advertising and/or targeted advertising using thesystems and methods disclosed herein. Thus, the system 100 of FIG. 1preferably includes an audience measurement system that monitors mediaexposure in the household in order to count advertisement exposurethrough general broadcasts. Such an audience measurement systems may beany type of system such as the system(s) employed by the NielsenCompany, LLC, to develop television ratings and/or to performadvertisement broadcast monitoring (e.g., the Monitor-Plus® system).

Based on the collected data for the household A 102 and/or extrapolationto the household A 102 if the household A 102 is not directly monitoredby an audience measurement system, the advertisement selector 202determines a net effectiveness metric for the selected advertisement torepresent the saturation of the household A 102 (block 912). Forexample, if using discrete saturation levels, the advertisement selector202 may apply a penalty to the advertisement (e.g., subtract thesaturation metric from the corresponding product opportunity metric)using a function of the number of times the selected advertisement hasbeen shown (or is estimated to have been shown) to the household A 102and the number of times that an advertisement must be shown to thehousehold A 102 before saturation begins. If the advertisement usesbinary saturation levels, the advertisement selector 202 may apply ahigh penalty to (e.g., disqualify) an advertisement to which thehousehold A 102 is saturated and apply no penalty to an advertisement towhich the household A 102 is not saturated. The penalty may be appliedby subtracting a value associated with the penalty from thecorresponding opportunity metric for the product in question.

After determining the saturation metric and the net effectivenessmetric, the advertisement selector 202 determines whether the neteffectiveness metric (i.e., the opportunity metric as modified by thepenalty) is sufficiently high for the advertisement to overcome thesaturation of the household A 102 (block 914). For example, theadvertisement selector 202 may determine if the net effectiveness metricfor the advertisement is the highest net effectiveness metric calculatedfor the household A 102. If the opportunity for a product is stillsufficiently high at the household A 102, the net effectiveness metricis sufficiently high and the selected advertisement may still influencepurchasing decisions despite having been presented to the household A102 a number of times.

After determining the net effectiveness metric, the advertisementselector 202 determines whether there are additional advertisements thatmay be selected for the selected product (block 914). In some examples,a first advertisement for a selected product may have reached saturationat the household A 102, but a second advertisement for the selectedproduct has not yet reached saturation. If there are additionaladvertisements available for the selected product (block 914), controlreturns to block 908 to select another advertisement for the selectedproduct. In contrast, if there are no additional advertisementsavailable (block 914), the advertisement selector 202 determines whetherthere are additional products for which an opportunity metric isavailable (block 916).

If, at block 916, there are no additional products, the advertisementselector 202 identifies an advertisement for a product, based on the neteffectiveness metrics and one or more thresholds, to the media deliverer204 for transmission to the selected household A 102 (block 918). Forexample, the advertisement selector 202 may identify one or moreadvertisements that have a net effectiveness metric greater than athreshold. In contrast, if one or more advertisements are less than thethreshold, they may not be identified as acceptable for delivery to thehousehold A 102. In some examples, the threshold is based on the reach,the lower limit of effectiveness desired by an advertiser, and/or otherfactors that may be defined in an advertising agreement. When theadvertisement selector 202 has identified an advertisement for theselected household A 102 (block 918), the instructions 900 may end andcontrol returns to block 706 of FIG. 7.

FIG. 10 is a flowchart representative of example machine readableinstructions 1000 which may be executed to determine a media response ofa household to a delivered advertisement. The example instructions 1000may be executed by, for example, the processor 1102 of FIG. 11 toimplement the media response evaluator 212 of FIG. 2.

The media evaluator 212 receives a media response characteristic from,for example, a media research organization or other media responsecharacteristic provider (block 1002). In some examples, the mediaresponse characteristic is determined by statistical models thatdescribe the effectiveness of an advertisement per presentation as acharacteristic of the number of times that the advertisement ispresented to a given person. In some models, as the number of times theadvertisement is shown to a household increases, the effectiveness ofeach additional showing decreases (e.g., f(n)˜1/n, where f(n) is theeffectiveness of the next presentation and n is the total number ofpresentations) and/or the overall effectiveness of all presentations ofthe advertisement flattens out (e.g., t(n)˜log(n), where t(n) is thetotal effectiveness of all presentations and n is the total number ofpresentations). However, in some other models such as the exampleadvertisement saturation characteristic 324 of FIG. 3B, theeffectiveness f(n) resembles an S-shaped curve where the greatesteffectiveness lies after the first presentation of an advertisement.

In some examples, the media evaluator 212 deduces a media responsecharacteristic of the household A 102 based on the changing purchases ofthe household A 102, the advertisements presented to the household A102, the segment of the household A 102, and/or the media responsecharacteristic of the segment of the household A 102. For example, somehouseholds may respond to advertising more readily and saturate withrespect to a given advertisement more quickly than average. Thus, it maybe desirable to avoid repeating advertisements to such households veryoften, and new advertisements could potentially command a higher pricefor presentation than repeated advertisements. In contrast, somehouseholds may be less prone to saturation with respect to any givenadvertisement than average. In such a case, the media evaluator 212 maypenalize advertisements less than for typical households for the samenumber of presentations.

The media evaluator 212 receives an identification of an advertisementdelivered to the household A 102 (block 1004). For example, the mediaevaluator 212 may be notified via metadata that an advertisement wasdelivered to the household A 102 at a particular time. The mediaevaluator 212 determines the number of times the advertisement has beenshown to the household A 102 in a measured period (e.g., one day, oneweek, two weeks, one month, etc.) (block 1006). Based on the number oftimes the advertisement has been shown (e.g., including the most recentpresentation) and the media response characteristic, the media evaluator212 determines a media response value of the household A 102 to futurepresentations of the advertisement (block 1008). The media responsevalues may be stored for future reference by the advertisement selector202. In some examples, the media evaluator 212 only determines thehousehold media response on demand when the advertisement selector 202selects an advertisement for potential delivery based on a product witha high opportunity metric. In such examples, the media evaluator 212sends the future response to the advertisement selector 202. When themedia evaluator 212 has predicted the future response, the exampleinstructions 1000 end and control returns to block 710 of FIG. 7.

FIG. 11 is a diagram of an example processor system 1100 that may beused to execute the example machine readable instructions 700, 800, 900,and/or 1000 described in FIGS. 7-10, as well as to implement the system100 of FIGS. 1-3A and the headend system 200 described in FIG. 2. Theexample processor system 1100 includes a processor 1102 havingassociated memories, such as a random access memory (RAM) 1104, a readonly memory (ROM) 1106 and a flash memory 1108. The processor 1102 incommunication with an interface, such as a bus 1112 to which othercomponents may be interfaced. In the illustrated example, the componentsinterfaced to the bus 1112 include an input device 1114, a displaydevice 1116, a mass storage device 1118, a removable storage devicedrive 1120, and a network adapter 1122. The removable storage devicedrive 1120 may include associated removable storage media 1124 such asmagnetic or optical media. The network adapter 1122 may connect theprocessor system 1100 to an external network 1126.

The example processor system 1100 may be, for example, a desktoppersonal computer, a notebook computer, a workstation or any othercomputing device. The processor 1102 may be any type of logic device,such as a microprocessor from the Intel® Pentium® family ofmicroprocessors, the Intel® Itanium® family of microprocessors, and/orthe Intel XScale® family of processors. The memories 1104, 1106 and 1108that are in communication with the processor 1102 may be any suitablememory devices and may be sized to fit the storage demands of the system1100. In particular, the flash memory 1108 may be a non-volatile memorythat is accessed and erased on a block-by-block basis.

The input device 1114 may be implemented using a keyboard, a mouse, atouch screen, a track pad, a barcode scanner or any other device thatenables a user to provide information to the processor 1102.

The display device 1116 may be, for example, a liquid crystal display(LCD) monitor, a cathode ray tube (CRT) monitor or any other suitabledevice that acts as an interface between the processor 1102 and a user.The display device 1116 includes any additional hardware required tointerface a display screen to the processor 1102.

The mass storage device 1118 may be, for example, a hard drive or anyother magnetic, optical, or solid state media that is readable by theprocessor 1102.

The removable storage device drive 1120 may, for example, be an opticaldrive, such as a compact disk-recordable (CD-R) drive, a compactdisk-rewritable (CD-RW) drive, a digital versatile disk (DVD) drive orany other optical drive. It may alternatively be, for example, amagnetic media drive and/or a solid state universal serial bus (USB)storage drive. The removable storage media 1124 is complimentary to theremovable storage device drive 1120, inasmuch as the media 1124 isselected to operate with the drive 1120. For example, if the removablestorage device drive 1120 is an optical drive, the removable storagemedia 1124 may be a CD-R disk, a CD-RW disk, a DVD disk or any othersuitable optical disk. On the other hand, if the removable storagedevice drive 1120 is a magnetic media device, the removable storagemedia 1124 may be, for example, a diskette or any other suitablemagnetic storage media.

The network adapter 1122 may be, for example, an Ethernet adapter, awireless local area network (LAN) adapter, a telephony modem, or anyother device that allows the processor system 1100 to communicate withone or more other processor systems over a network. The external network1126 may be a LAN, a wide area network (WAN), a wireless network, or anytype of network capable of communicating with the processor system 1100.Example networks may include the Internet, an intranet, and/or an ad hocnetwork.

Although this patent discloses example systems including software orfirmware executed on hardware, it should be noted that such systems aremerely illustrative and should not be considered as limiting. Forexample, it is contemplated that any or all of these hardware andsoftware components could be implemented exclusively in hardware,exclusively in software, exclusively in firmware or in some combinationof hardware, firmware and/or software. Accordingly, while the abovespecification described example systems, methods and articles ofmanufacture, the examples are not the only way to implement suchsystems, methods and articles of manufacture. Therefore, althoughcertain example methods, apparatus and articles of manufacture have beendescribed herein, the scope of coverage of this patent is not limitedthereto. On the contrary, this patent covers all methods, apparatus andarticles of manufacture fairly falling within the scope of the claimseither literally or under the doctrine of equivalents.

What is claimed is:
 1. A media provider headend system comprising:memory including machine readable instructions; and a processor toexecute the instructions to at least: access an address of a mediadevice associated with a first household; access first purchase datareported from a logging device that is to log product purchase activityat the first household; determine a consumer segment associated with thefirst household; determine, based on the first purchase data, an actualquantity of a first product purchased by the first household during afirst period of time; estimate, based on second purchase data associatedwith a plurality of consumers included in the consumer segmentassociated with the first household, an estimated quantity of the firstproduct to be purchased by the first household during the first periodof time; determine an opportunity metric for the first product based onthe actual quantity of the first product purchased by the firsthousehold during the first period of time and the estimated quantity ofthe first product to be purchased by the first household during thefirst period of time; select a first advertisement associated with thefirst product to deliver to the media device based on the opportunitymetric and a saturation metric associated with the first household forthe first advertisement, the saturation metric determined based ondetection of identification codes embedded in prior transmissions of thefirst advertisement to the media device; and transmit the firstadvertisement to the address of the media device.
 2. The media providerheadend system of claim 1, wherein the media device corresponds to a settop box, and the address corresponds to at least one a media accesscontrol address or an Internet protocol address.
 3. The media providerheadend system of claim 1, wherein the processor is to determine theconsumer segment associated with the first household based ondemographic data obtained for the first household.
 4. The media providerheadend system of claim 1, wherein the processor is to determine theopportunity metric based on a difference between the estimated quantityand the actual quantity.
 5. The media provider headend system of claim1, wherein the opportunity metric is a first opportunity metric, and theprocessor is to: access point-of-sale data to identify a second productassociated with the first product; and determine a second opportunitymetric for the second product to be purchased by the first householdbased on the first purchase data reported from the logging device andthe second purchase data associated with the plurality of consumersincluded in the consumer segment.
 6. The media provider headend systemof claim 1, wherein to select the first advertisement, the processor isto: determine the saturation metric based on a number of the priortransmissions of the first advertisement received by the media device;adjust the opportunity metric based on the saturation metric todetermine an effectiveness metric for the first advertisement; andselect the first advertisement when the effectiveness metric satisfies athreshold.
 7. The media provider headend system of claim 6, wherein todetermine the effectiveness metric, the processor is to subtract thesaturation metric from the opportunity metric.
 8. A storage device orstorage disk comprising machine readable instructions that, whenexecuted, cause a processor to at least: access an address of a mediadevice associated with a first household; access first purchase datareported from a logging device that is to log product purchase activityat the first household; determine a consumer segment associated with thefirst household; determine, based on the first purchase data, an actualquantity of a first product purchased by the first household during afirst period of time; estimate, based on second purchase data associatedwith a plurality of consumers included in the consumer segmentassociated with the first household, an estimated quantity of the firstproduct to be purchased by the first household during the first periodof time; determine an opportunity metric for the first product based onthe actual quantity of the first product purchased by the firsthousehold during the first period of time and the estimated quantity ofthe first product to be purchased by the first household during thefirst period of time; select a first advertisement associated with thefirst product to deliver to the media device based on the opportunitymetric and a saturation metric associated with the first household forthe first advertisement, the saturation metric determined based ondetection of identification codes embedded in prior transmissions of thefirst advertisement to the media device; and transmit the firstadvertisement to the address of the media device.
 9. The storage deviceor storage disk of claim 8, wherein the media device corresponds to aset top box, and the address corresponds to at least one a media accesscontrol address or an Internet protocol address.
 10. The storage deviceor storage disk of claim 8, wherein the instructions, when executed,cause the processor to determine the consumer segment associated withthe first household based on demographic data obtained for the firsthousehold.
 11. The storage device or storage disk of claim 8, whereinthe instructions, when executed, cause the processor to determine theopportunity metric based on a difference between the estimated quantityand the actual quantity.
 12. The storage device or storage disk of claim8, wherein the opportunity metric is a first opportunity metric, and theinstructions, when executed, cause the processor to: accesspoint-of-sale data to identify a second product associated with thefirst product; and determine a second opportunity metric for the secondproduct to be purchased by the first household based on the firstpurchase data reported from the logging device and the second purchasedata associated with the plurality of consumers included in the consumersegment.
 13. The storage device or storage disk of claim 8, wherein theinstructions, when executed, cause the processor to select the firstadvertisement by: determining the saturation metric based on a number ofthe prior transmissions of the first advertisement received by the mediadevice; adjusting the opportunity metric based on the saturation metricto determine an effectiveness metric for the first advertisement; andselecting the first advertisement when the effectiveness metricsatisfies a threshold.
 14. The storage device or storage disk of claim13, wherein the instructions, when executed, cause the processor tosubtract the saturation metric from the opportunity metric to determinethe effectiveness metric.
 15. A method comprising: accessing an addressof a media device associated with a first household; accessing firstpurchase data reported from a logging device that is to log productpurchase activity at the first household; determining, by executing aninstruction with a processor, a consumer segment associated with thefirst household; determining, by executing an instruction with theprocessor, and based on the first purchase data, an actual quantity of afirst product purchased by the first household during a first period oftime; estimating, by executing an instruction with the processor, andbased on second purchase data associated with a plurality of consumersincluded in the consumer segment associated with the first household, anestimated quantity of the first product to be purchased by the firsthousehold during the first period of time; determining, by executing aninstruction with the processor, an opportunity metric for the firstproduct based on the actual quantity of the first product purchased bythe first household during the first period of time and the estimatedquantity of the first product to be purchased by the first householdduring the first period of time; selecting, by executing an instructionwith the processor, a first advertisement associated with the firstproduct to deliver to the media device based on the opportunity metricand a saturation metric associated with the first household for thefirst advertisement, the saturation metric determined based on detectionof identification codes embedded in prior transmissions of the firstadvertisement to the media device; and transmitting the firstadvertisement to the address of the media device.
 16. The method ofclaim 15, wherein the media device corresponds to a set top box, and theaddress corresponds to at least one a media access control address or anInternet protocol address.
 17. The method of claim 15, further includingdetermining the consumer segment associated with the first householdbased on demographic data obtained for the first household.
 18. Themethod of claim 15, wherein the determining of the opportunity metricincludes determining the opportunity metric based on a differencebetween the estimated quantity and the actual quantity.
 19. The methodof claim 15, wherein the opportunity metric is a first opportunitymetric, and further including: accessing point-of-sale data to identifya second product associated with the first product; and determining asecond opportunity metric for the second product to be purchased by thefirst household based on the first purchase data reported from thelogging device and the second purchase data associated with theplurality of consumers included in the consumer segment.
 20. The methodof claim 15, wherein the selecting of the first advertisement includes:determining the saturation metric based on a number of the priortransmissions of the first advertisement received by the media device;adjusting the opportunity metric based on the saturation metric todetermine an effectiveness metric for the first advertisement; andselecting the first advertisement when the effectiveness metricsatisfies a threshold.
 21. The method of claim 20, wherein the adjustingof the opportunity metric based on the saturation metric to determinethe effectiveness metric includes subtracting the saturation metric fromthe opportunity metric to determine the effectiveness metric.